muxamilian / duralava Goto Github PK

When changing img_size = 64 to count for images with different width and height i.e., also changing the process_img to:

def process_img(file_path, img_w, img_h):
  img = tf.io.read_file(file_path)
  img = tf.image.decode_jpeg(img, channels=3)
  img = tf.image.convert_image_dtype(img, tf.float32)
  img = tf.image.resize(img, size=(img_h, img_w), method='area')
  img = tf.image.convert_image_dtype(img, tf.uint8)
  return img

and the definition of the discriminator to:

def make_discriminator_model():
  disc = tf.keras.Sequential(
    [
      layers.Input(shape=(img_h, img_w, 3)),
...

and line 391 to: all_images.append(process_img(item, img_w, img_h))

The training crashes after the first epoch with: ValueError: Cannot reshape a tensor with 49152 elements to shape [32,128] (4096 elements) for '{{node Reshape}} = Reshape[T=DT_FLOAT, Tshape=DT_INT32](discriminator/flatten/Reshape, Reshape/shape)' with input shapes: [32,1536], [2] and with input tensors computed as partial shapes: input[1] = [32,128].

Apparently, there is a problem here: real_first_output = tf.reshape(self.discriminator(images[i,...], training=True), (batch_size, disc_dim)). Somehow, the discriminator is not outputting correct dimension for reshaping. I have doubts that something is wrong with disc_dim but couldn't solve it.

I am using:

img_w = 256
img_h = 144